Overview

Brought to you by YData

Dataset statistics

Number of variables8
Number of observations807
Missing cells0
Missing cells (%)0.0%
Duplicate rows156
Duplicate rows (%)19.3%
Total size in memory50.6 KiB
Average record size in memory64.2 B

Variable types

Numeric8

Alerts

Dataset has 156 (19.3%) duplicate rowsDuplicates
Battery_Power is highly overall correlated with Mobile_Size and 2 other fieldsHigh correlation
Mobile_Size is highly overall correlated with Battery_Power and 3 other fieldsHigh correlation
Price is highly overall correlated with Battery_Power and 3 other fieldsHigh correlation
ROM is highly overall correlated with Mobile_Size and 1 other fieldsHigh correlation
Ratings is highly overall correlated with Battery_Power and 2 other fieldsHigh correlation

Reproduction

Analysis started2024-12-11 11:11:21.580017
Analysis finished2024-12-11 11:11:24.400166
Duration2.82 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Ratings
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1126394
Minimum2.8
Maximum4.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2024-12-11T16:41:24.434332image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2.8
5-th percentile3.5
Q13.9
median4.1
Q34.4
95-th percentile4.6
Maximum4.8
Range2
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.36783027
Coefficient of variation (CV)0.089438978
Kurtosis-0.28282511
Mean4.1126394
Median Absolute Deviation (MAD)0.3
Skewness-0.55938623
Sum3318.9
Variance0.1352991
MonotonicityNot monotonic
2024-12-11T16:41:24.484933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
4.5 135
16.7%
4.1 96
11.9%
4.4 91
11.3%
3.9 72
8.9%
4.3 66
8.2%
3.8 61
7.6%
4.2 55
6.8%
4 40
 
5.0%
3.7 36
 
4.5%
3.6 33
 
4.1%
Other values (10) 122
15.1%
ValueCountFrequency (%)
2.8 2
 
0.2%
3 4
 
0.5%
3.1 1
 
0.1%
3.2 3
 
0.4%
3.3 2
 
0.2%
3.4 27
3.3%
3.5 32
4.0%
3.6 33
4.1%
3.7 36
4.5%
3.8 61
7.6%
ValueCountFrequency (%)
4.8 1
 
0.1%
4.7 25
 
3.1%
4.6 25
 
3.1%
4.5 135
16.7%
4.4 91
11.3%
4.3 66
8.2%
4.2 55
6.8%
4.1 96
11.9%
4 40
 
5.0%
3.9 72
8.9%

RAM
Real number (ℝ)

Distinct10
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9417596
Minimum0
Maximum12
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2024-12-11T16:41:24.531702image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q16
median6
Q36
95-th percentile8
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.0563593
Coefficient of variation (CV)0.34608592
Kurtosis1.5350452
Mean5.9417596
Median Absolute Deviation (MAD)0
Skewness0.26642688
Sum4795
Variance4.2286138
MonotonicityNot monotonic
2024-12-11T16:41:24.576242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
6 421
52.2%
8 153
 
19.0%
4 113
 
14.0%
3 39
 
4.8%
12 27
 
3.3%
2 21
 
2.6%
1 20
 
2.5%
10 7
 
0.9%
5 4
 
0.5%
0 2
 
0.2%
ValueCountFrequency (%)
0 2
 
0.2%
1 20
 
2.5%
2 21
 
2.6%
3 39
 
4.8%
4 113
 
14.0%
5 4
 
0.5%
6 421
52.2%
8 153
 
19.0%
10 7
 
0.9%
12 27
 
3.3%
ValueCountFrequency (%)
12 27
 
3.3%
10 7
 
0.9%
8 153
 
19.0%
6 421
52.2%
5 4
 
0.5%
4 113
 
14.0%
3 39
 
4.8%
2 21
 
2.6%
1 20
 
2.5%
0 2
 
0.2%

ROM
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.390335
Minimum2
Maximum256
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2024-12-11T16:41:24.623645image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile16
Q132
median32
Q364
95-th percentile128
Maximum256
Range254
Interquartile range (IQR)32

Descriptive statistics

Standard deviation53.868626
Coefficient of variation (CV)0.8365949
Kurtosis4.0785335
Mean64.390335
Median Absolute Deviation (MAD)28
Skewness1.9408603
Sum51963
Variance2901.8288
MonotonicityNot monotonic
2024-12-11T16:41:24.676407image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
32 311
38.5%
64 224
27.8%
128 137
17.0%
256 33
 
4.1%
16 29
 
3.6%
4 25
 
3.1%
24 14
 
1.7%
25 9
 
1.1%
20 3
 
0.4%
12 3
 
0.4%
Other values (10) 19
 
2.4%
ValueCountFrequency (%)
2 1
 
0.1%
3 3
 
0.4%
4 25
3.1%
8 3
 
0.4%
10 1
 
0.1%
12 3
 
0.4%
15 1
 
0.1%
16 29
3.6%
20 3
 
0.4%
22 1
 
0.1%
ValueCountFrequency (%)
256 33
 
4.1%
128 137
17.0%
64 224
27.8%
56 2
 
0.2%
51 1
 
0.1%
40 3
 
0.4%
35 3
 
0.4%
32 311
38.5%
25 9
 
1.1%
24 14
 
1.7%

Mobile_Size
Real number (ℝ)

High correlation 

Distinct47
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6206605
Minimum2
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2024-12-11T16:41:24.728112image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.5
Q14.5
median4.77
Q36.3
95-th percentile6.67
Maximum44
Range42
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation3.9574086
Coefficient of variation (CV)0.70408248
Kurtosis85.457977
Mean5.6206605
Median Absolute Deviation (MAD)0.27
Skewness9.058579
Sum4535.873
Variance15.661083
MonotonicityNot monotonic
2024-12-11T16:41:24.783166image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
4.5 182
22.6%
4.54 139
17.2%
4.77 56
 
6.9%
6.5 37
 
4.6%
6.7 28
 
3.5%
6.4 24
 
3.0%
6.2 23
 
2.9%
5.5 19
 
2.4%
6.44 18
 
2.2%
6.39 18
 
2.2%
Other values (37) 263
32.6%
ValueCountFrequency (%)
2 8
 
1.0%
3.7 15
 
1.9%
4.4 17
 
2.1%
4.5 182
22.6%
4.503 1
 
0.1%
4.52 3
 
0.4%
4.54 139
17.2%
4.57 2
 
0.2%
4.58 13
 
1.6%
4.7 18
 
2.2%
ValueCountFrequency (%)
44 8
 
1.0%
7 2
 
0.2%
6.7 28
3.5%
6.67 13
 
1.6%
6.6 4
 
0.5%
6.59 4
 
0.5%
6.55 4
 
0.5%
6.53 11
 
1.4%
6.52 2
 
0.2%
6.5 37
4.6%

Primary_Cam
Real number (ℝ)

Distinct11
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.821561
Minimum5
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2024-12-11T16:41:24.827057image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile35
Q148
median48
Q348
95-th percentile64
Maximum64
Range59
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.155916
Coefficient of variation (CV)0.23328215
Kurtosis1.5820465
Mean47.821561
Median Absolute Deviation (MAD)0
Skewness-0.63585453
Sum38592
Variance124.45447
MonotonicityNot monotonic
2024-12-11T16:41:24.867726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
48 446
55.3%
64 171
 
21.2%
35 79
 
9.8%
38 71
 
8.8%
25 11
 
1.4%
8 9
 
1.1%
16 6
 
0.7%
20 5
 
0.6%
26 4
 
0.5%
40 3
 
0.4%
ValueCountFrequency (%)
5 2
 
0.2%
8 9
 
1.1%
16 6
 
0.7%
20 5
 
0.6%
25 11
 
1.4%
26 4
 
0.5%
35 79
 
9.8%
38 71
 
8.8%
40 3
 
0.4%
48 446
55.3%
ValueCountFrequency (%)
64 171
 
21.2%
48 446
55.3%
40 3
 
0.4%
38 71
 
8.8%
35 79
 
9.8%
26 4
 
0.5%
25 11
 
1.4%
20 5
 
0.6%
16 6
 
0.7%
8 9
 
1.1%

Selfi_Cam
Real number (ℝ)

Distinct21
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.8686493
Minimum0
Maximum23
Zeros5
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2024-12-11T16:41:24.908623image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18
median8
Q312
95-th percentile20
Maximum23
Range23
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.5472535
Coefficient of variation (CV)0.51273349
Kurtosis0.95534988
Mean8.8686493
Median Absolute Deviation (MAD)3
Skewness0.79226801
Sum7157
Variance20.677515
MonotonicityNot monotonic
2024-12-11T16:41:24.952379image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
8 374
46.3%
5 100
 
12.4%
12 78
 
9.7%
13 64
 
7.9%
2 60
 
7.4%
20 31
 
3.8%
1 21
 
2.6%
15 20
 
2.5%
16 9
 
1.1%
11 8
 
1.0%
Other values (11) 42
 
5.2%
ValueCountFrequency (%)
0 5
 
0.6%
1 21
 
2.6%
2 60
 
7.4%
4 1
 
0.1%
5 100
 
12.4%
6 1
 
0.1%
7 5
 
0.6%
8 374
46.3%
10 2
 
0.2%
11 8
 
1.0%
ValueCountFrequency (%)
23 6
 
0.7%
22 7
 
0.9%
21 2
 
0.2%
20 31
3.8%
18 5
 
0.6%
17 1
 
0.1%
16 9
 
1.1%
15 20
 
2.5%
14 7
 
0.9%
13 64
7.9%

Battery_Power
Real number (ℝ)

High correlation 

Distinct49
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3278.86
Minimum1020
Maximum6000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2024-12-11T16:41:25.001642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1020
5-th percentile1200
Q13000
median3000
Q33800
95-th percentile4780
Maximum6000
Range4980
Interquartile range (IQR)800

Descriptive statistics

Standard deviation937.6288
Coefficient of variation (CV)0.28596183
Kurtosis0.52935522
Mean3278.86
Median Absolute Deviation (MAD)500
Skewness-0.24451162
Sum2646040
Variance879147.77
MonotonicityNot monotonic
2024-12-11T16:41:25.056100image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
3000 262
32.5%
3500 111
13.8%
3800 98
 
12.1%
4700 48
 
5.9%
2500 37
 
4.6%
5000 33
 
4.1%
1750 28
 
3.5%
1050 19
 
2.4%
2800 19
 
2.4%
1200 17
 
2.1%
Other values (39) 135
16.7%
ValueCountFrequency (%)
1020 4
 
0.5%
1050 19
2.4%
1080 4
 
0.5%
1100 1
 
0.1%
1200 17
2.1%
1500 3
 
0.4%
1550 7
 
0.9%
1750 28
3.5%
1900 4
 
0.5%
2000 7
 
0.9%
ValueCountFrequency (%)
6000 6
 
0.7%
5000 33
4.1%
4900 1
 
0.1%
4780 2
 
0.2%
4700 48
5.9%
4600 1
 
0.1%
4550 1
 
0.1%
4440 8
 
1.0%
4300 1
 
0.1%
4230 11
 
1.4%

Price
Real number (ℝ)

High correlation 

Distinct241
Distinct (%)29.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14269.167
Minimum479
Maximum153000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 KiB
2024-12-11T16:41:25.108166image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum479
5-th percentile649
Q1984
median1699
Q318994.5
95-th percentile53990
Maximum153000
Range152521
Interquartile range (IQR)18010.5

Descriptive statistics

Standard deviation23092.74
Coefficient of variation (CV)1.6183663
Kurtosis10.796041
Mean14269.167
Median Absolute Deviation (MAD)1060
Skewness2.9527044
Sum11515218
Variance5.3327464 × 108
MonotonicityNot monotonic
2024-12-11T16:41:25.163587image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
649 30
 
3.7%
799 18
 
2.2%
1199 18
 
2.2%
1099 17
 
2.1%
1299 15
 
1.9%
24999 14
 
1.7%
1349 14
 
1.7%
599 12
 
1.5%
18999 11
 
1.4%
749 10
 
1.2%
Other values (231) 648
80.3%
ValueCountFrequency (%)
479 1
 
0.1%
539 1
 
0.1%
559 1
 
0.1%
595 1
 
0.1%
599 12
 
1.5%
629 6
 
0.7%
639 10
 
1.2%
645 1
 
0.1%
649 30
3.7%
650 2
 
0.2%
ValueCountFrequency (%)
153000 1
 
0.1%
140300 5
0.6%
121300 2
 
0.2%
117100 6
0.7%
112450 1
 
0.1%
106600 2
 
0.2%
103000 1
 
0.1%
84900 1
 
0.1%
77999 6
0.7%
75000 1
 
0.1%

Interactions

2024-12-11T16:41:23.962438image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:21.653850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:22.119721image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:22.430957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:22.738257image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:23.040811image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:23.348538image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:23.649082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:24.007979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:21.758744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:22.163110image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:22.472543image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:22.780669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:23.082841image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:23.390636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:23.691717image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:24.048805image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:21.834961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:22.201146image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:22.511577image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:22.818331image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:23.121880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:23.427519image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:23.731381image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:24.090141image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:21.905157image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:22.239459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:22.547451image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:22.855324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:23.158899image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:23.463913image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:23.768769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:24.129048image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:21.952919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:22.275284image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:22.583930image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:22.889666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:23.196170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:23.498452image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:23.805200image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:24.170228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:21.994472image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:22.313078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:22.621135image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:22.926372image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:23.232868image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:23.534958image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:23.844553image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:24.209023image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:22.033085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:22.350190image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:22.657144image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:22.962340image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:23.268689image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:23.569819image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:23.880957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:24.250924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:22.074837image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:22.388644image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:22.696563image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:22.998667image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:23.306880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:23.606894image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-11T16:41:23.919572image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-12-11T16:41:25.203152image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Battery_PowerMobile_SizePricePrimary_CamRAMROMRatingsSelfi_Cam
Battery_Power1.0000.5980.560-0.1130.0540.4500.515-0.088
Mobile_Size0.5981.0000.796-0.2710.1210.5780.626-0.184
Price0.5600.7961.000-0.2910.1440.5380.710-0.077
Primary_Cam-0.113-0.271-0.2911.0000.137-0.133-0.2160.179
RAM0.0540.1210.1440.1371.0000.3890.1580.238
ROM0.4500.5780.538-0.1330.3891.0000.420-0.017
Ratings0.5150.6260.710-0.2160.1580.4201.000-0.051
Selfi_Cam-0.088-0.184-0.0770.1790.238-0.017-0.0511.000

Missing values

2024-12-11T16:41:24.315185image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-11T16:41:24.373750image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

RatingsRAMROMMobile_SizePrimary_CamSelfi_CamBattery_PowerPrice
04.34.0128.06.004813.0400024999
13.46.064.04.504812.0400015999
24.34.04.04.506416.0400015000
34.46.064.06.404815.0380018999
44.56.0128.06.183515.0380018999
54.78.0128.05.803512.05000140300
64.48.0128.06.70645.0470029999
74.58.0128.06.704812.0470047999
84.44.0128.06.53482.0402016490
94.58.0256.06.18355.0380022999
RatingsRAMROMMobile_SizePrimary_CamSelfi_CamBattery_PowerPrice
7973.56.032.04.52488.035002599
7984.78.064.06.503512.03500117100
7994.64.064.06.20358.0350053990
8004.46.0128.06.394812.0380031999
8013.66.032.04.50488.03000898
8023.86.032.04.544812.028001299
8034.18.064.04.54648.025001390
8044.43.032.06.20481.038009790
8053.710.032.04.50648.03500799
8063.56.032.04.506415.01050799

Duplicate rows

Most frequently occurring

RatingsRAMROMMobile_SizePrimary_CamSelfi_CamBattery_PowerPrice# duplicates
1294.56.0128.06.394813.03800249997
1384.58.0128.06.704812.04700479997
1214.56.032.04.50648.030006496
1394.58.0128.06.70641.04700779996
1034.46.064.06.40485.03800189995
1404.58.0256.06.18355.03800229995
1464.64.064.06.20358.03500539905
1544.78.064.06.503512.035001171005
23.44.064.05.50485.03000139994
113.66.032.04.50488.030008984